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Prediction mechanisms that do not incentivize undesirable actions

Publication ,  Journal Article
Shi, P; Conitzer, V; Guo, M
Published in: Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
December 1, 2009
Published version (DOI)

A potential downside of prediction markets is that they may incentivize agents to take undesirable actions in the real world. For example, a prediction market for whether a terrorist attack will happen may incentivize terrorism, and an in-house prediction market for whether a product will be successfully released may incentivize sabotage. In this paper, we study principal-aligned prediction mechanisms-mechanisms that do not incentivize undesirable actions. We characterize all principal-aligned proper scoring rules, and we show an "overpayment" result, which roughly states that with n agents, any prediction mechanism that is principal-aligned will, in the worst case, require the principal to pay Θ(n) times as much as a mechanism that is not. We extend our model to allow uncertainties about the principal's utility and restrictions on agents' actions, showing a richer characterization and a similar "overpayment" result. © 2009 Springer-Verlag Berlin Heidelberg.

Duke Scholars

Vincent Conitzer
Author Vincent Conitzer Computer Science

Published In

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

DOI

10.1007/978-3-642-10841-9_10

EISSN

1611-3349

ISSN

0302-9743

Publication Date

December 1, 2009

Volume

5929 LNCS

Start / End Page

89 / 100

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Shi, P., Conitzer, V., & Guo, M. (2009). Prediction mechanisms that do not incentivize undesirable actions. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 5929 LNCS, 89–100. https://doi.org/10.1007/978-3-642-10841-9_10
Shi, P., V. Conitzer, and M. Guo. “Prediction mechanisms that do not incentivize undesirable actions.” Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 5929 LNCS (December 1, 2009): 89–100. https://doi.org/10.1007/978-3-642-10841-9_10.
Shi P, Conitzer V, Guo M. Prediction mechanisms that do not incentivize undesirable actions. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. 2009 Dec 1;5929 LNCS:89–100.
Shi, P., et al. “Prediction mechanisms that do not incentivize undesirable actions.” Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol. 5929 LNCS, Dec. 2009, pp. 89–100. Scopus, doi:10.1007/978-3-642-10841-9_10.
Shi P, Conitzer V, Guo M. Prediction mechanisms that do not incentivize undesirable actions. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. 2009 Dec 1;5929 LNCS:89–100.

Published In

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

DOI

10.1007/978-3-642-10841-9_10

EISSN

1611-3349

ISSN

0302-9743

Publication Date

December 1, 2009

Volume

5929 LNCS

Start / End Page

89 / 100

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences